import warnings
warnings.filterwarnings('ignore')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import missingno as msno
#from ydata_profiling import ProfileReport
from sklearn import set_config
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder
from sklearn.compose import ColumnTransformer, make_column_selector
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.feature_selection import SelectKBest, f_classif, RFE
from sklearn.decomposition import PCA
from sklearn.cluster import DBSCAN, KMeans
from sklearn.metrics import silhouette_score, accuracy_score, confusion_matrix, precision_score, recall_score, f1_score, roc_curve, auc
from pyclustering.cluster.clarans import clarans;
from pyclustering.utils import timedcall;
import tensorflow as tf
from keras.models import Model
from keras.layers import Dense, concatenate
import xgboost as xgb
from xgboost import XGBClassifier

WARNING:tensorflow:From C:\Anaconda\Lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.

telecom = pd.read_csv('telecom_customer_churn.csv')
telecom.head(3)

originalData = telecom
originalData.head()

telecom.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 38 columns):
 #   Column                             Non-Null Count  Dtype  
---  ------                             --------------  -----  
 0   Customer ID                        7043 non-null   object 
 1   Gender                             7043 non-null   object 
 2   Age                                7043 non-null   int64  
 3   Married                            7043 non-null   object 
 4   Number of Dependents               7043 non-null   int64  
 5   City                               7043 non-null   object 
 6   Zip Code                           7043 non-null   int64  
 7   Latitude                           7043 non-null   float64
 8   Longitude                          7043 non-null   float64
 9   Number of Referrals                7043 non-null   int64  
 10  Tenure in Months                   7043 non-null   int64  
 11  Offer                              7043 non-null   object 
 12  Phone Service                      7043 non-null   object 
 13  Avg Monthly Long Distance Charges  6361 non-null   float64
 14  Multiple Lines                     6361 non-null   object 
 15  Internet Service                   7043 non-null   object 
 16  Internet Type                      5517 non-null   object 
 17  Avg Monthly GB Download            5517 non-null   float64
 18  Online Security                    5517 non-null   object 
 19  Online Backup                      5517 non-null   object 
 20  Device Protection Plan             5517 non-null   object 
 21  Premium Tech Support               5517 non-null   object 
 22  Streaming TV                       5517 non-null   object 
 23  Streaming Movies                   5517 non-null   object 
 24  Streaming Music                    5517 non-null   object 
 25  Unlimited Data                     5517 non-null   object 
 26  Contract                           7043 non-null   object 
 27  Paperless Billing                  7043 non-null   object 
 28  Payment Method                     7043 non-null   object 
 29  Monthly Charge                     7043 non-null   float64
 30  Total Charges                      7043 non-null   float64
 31  Total Refunds                      7043 non-null   float64
 32  Total Extra Data Charges           7043 non-null   int64  
 33  Total Long Distance Charges        7043 non-null   float64
 34  Total Revenue                      7043 non-null   float64
 35  Customer Status                    7043 non-null   object 
 36  Churn Category                     1869 non-null   object 
 37  Churn Reason                       1869 non-null   object 
dtypes: float64(9), int64(6), object(23)
memory usage: 2.0+ MB

telecom.hist(figsize=(15,15), xrot=0);

telecom.describe()

telecom.pivot_table(values='Monthly Charge', index='Contract', columns='Customer Status', aggfunc='mean')

telecom.pivot_table(values='Monthly Charge', index=['Contract', 'Multiple Lines'], columns='Customer Status', aggfunc='mean')

telecom.pivot_table(values='Monthly Charge', index=['Contract', 'Internet Service'], columns='Customer Status', aggfunc='mean')

telecom.pivot_table(values='Tenure in Months', index='Contract', columns='Customer Status', aggfunc='mean')

Customer_Stayed=telecom[telecom['Customer Status']=='Stayed'].Age
Customer_Churned=telecom[telecom['Customer Status']=='Churned'].Age
Customer_Joined=telecom[telecom['Customer Status']=='Joined'].Age

plt.xlabel('Age')
plt.ylabel('Customers Numbers')
plt.hist([Customer_Stayed,Customer_Churned,Customer_Joined],label=['Stayed','Churned','Joined'])

plt.title('Customers Behavior ',fontweight ="bold")
plt.legend();

sns.histplot(telecom['Age']);

telecom_corr = telecom.select_dtypes(include='number')
corr_data  = telecom_corr.corr()
plt.figure(figsize = (20,10))
sns.heatmap(corr_data, annot = True);

fig = px.histogram(telecom, x="Customer Status", template ='xgridoff',barmode = "group", title = "<b>Customer Status Distribution<b>")
fig.update_layout(width=400, height=400, bargap=0.2)
fig.show()

pd.crosstab(telecom['Customer Status'], telecom['Married']).plot(kind='bar');

pd.crosstab(telecom['Customer Status'], telecom['Gender']).plot(kind='bar')

<Axes: xlabel='Customer Status'>

sns.countplot(data=telecom, x='Customer Status', hue='Internet Type');

fig,axes=plt.subplots(7,2,figsize=(10,20))
sns.countplot(x="Gender",hue='Customer Status',data=telecom,ax=axes[0,0])
sns.countplot(x="Married",hue='Customer Status',data=telecom,ax=axes[0,1])
sns.countplot(x="Phone Service",hue='Customer Status',data=telecom,ax=axes[1,0])
sns.countplot(x="Multiple Lines",hue='Customer Status',data=telecom,ax=axes[1,1])
sns.countplot(x="Internet Service",hue='Customer Status',data=telecom,ax=axes[2,0])
sns.countplot(x="Online Security",hue='Customer Status',data=telecom,ax=axes[2,1])
sns.countplot(x="Online Backup",hue='Customer Status',data=telecom,ax=axes[3,0])
sns.countplot(x="Device Protection Plan",hue='Customer Status',data=telecom,ax=axes[3,1])
sns.countplot(x="Premium Tech Support",hue='Customer Status',data=telecom,ax=axes[4,0])
sns.countplot(x="Streaming TV",hue='Customer Status',data=telecom,ax=axes[4,1])
sns.countplot(x="Streaming Movies",hue='Customer Status',data=telecom,ax=axes[5,0])
sns.countplot(x="Streaming Music",hue='Customer Status',data=telecom,ax=axes[5,1])
sns.countplot(x="Unlimited Data",hue='Customer Status',data=telecom,ax=axes[6,0])
sns.countplot(x="Paperless Billing",hue='Customer Status',data=telecom,ax=axes[6,1])
plt.tight_layout()
plt.show()

fig, ax = plt.subplots(figsize=(12, 8))
order = telecom[telecom['Customer Status'] == 'Churned']['Churn Reason'].value_counts().index
sns.countplot(data=telecom[telecom['Customer Status'] == 'Churned'], y='Churn Reason', hue='Customer Status', ax=ax, order=order)

plt.xlabel('Count')
plt.ylabel('Churn Reason')
plt.title('Count of Customer by Churn Reasons')

plt.show()

msno.matrix(telecom)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[21], line 1
----> 1 msno.matrix(telecom)

File C:\Anaconda\Lib\site-packages\missingno\missingno.py:72, in matrix(df, filter, n, p, sort, figsize, width_ratios, color, fontsize, labels, sparkline, inline, freq, ax)
     70 # Remove extraneous default visual elements.
     71 ax0.set_aspect('auto')
---> 72 ax0.grid(b=False)
     73 ax0.xaxis.tick_top()
     74 ax0.xaxis.set_ticks_position('none')

File C:\Anaconda\Lib\site-packages\matplotlib\axes\_base.py:3194, in _AxesBase.grid(self, visible, which, axis, **kwargs)
   3192 _api.check_in_list(['x', 'y', 'both'], axis=axis)
   3193 if axis in ['x', 'both']:
-> 3194     self.xaxis.grid(visible, which=which, **kwargs)
   3195 if axis in ['y', 'both']:
   3196     self.yaxis.grid(visible, which=which, **kwargs)

File C:\Anaconda\Lib\site-packages\matplotlib\axis.py:1698, in Axis.grid(self, visible, which, **kwargs)
   1695 if which in ['major', 'both']:
   1696     gridkw['gridOn'] = (not self._major_tick_kw['gridOn']
   1697                         if visible is None else visible)
-> 1698     self.set_tick_params(which='major', **gridkw)
   1699 self.stale = True

File C:\Anaconda\Lib\site-packages\matplotlib\axis.py:944, in Axis.set_tick_params(self, which, reset, **kwargs)
    931 """
    932 Set appearance parameters for ticks, ticklabels, and gridlines.
    933 
   (...)
    941     gridlines.
    942 """
    943 _api.check_in_list(['major', 'minor', 'both'], which=which)
--> 944 kwtrans = self._translate_tick_params(kwargs)
    946 # the kwargs are stored in self._major/minor_tick_kw so that any
    947 # future new ticks will automatically get them
    948 if reset:

File C:\Anaconda\Lib\site-packages\matplotlib\axis.py:1088, in Axis._translate_tick_params(kw, reverse)
   1086 for key in kw_:
   1087     if key not in allowed_keys:
-> 1088         raise ValueError(
   1089             "keyword %s is not recognized; valid keywords are %s"
   1090             % (key, allowed_keys))
   1091 kwtrans.update(kw_)
   1092 return kwtrans

ValueError: keyword grid_b is not recognized; valid keywords are ['size', 'width', 'color', 'tickdir', 'pad', 'labelsize', 'labelcolor', 'zorder', 'gridOn', 'tick1On', 'tick2On', 'label1On', 'label2On', 'length', 'direction', 'left', 'bottom', 'right', 'top', 'labelleft', 'labelbottom', 'labelright', 'labeltop', 'labelrotation', 'grid_agg_filter', 'grid_alpha', 'grid_animated', 'grid_antialiased', 'grid_clip_box', 'grid_clip_on', 'grid_clip_path', 'grid_color', 'grid_dash_capstyle', 'grid_dash_joinstyle', 'grid_dashes', 'grid_data', 'grid_drawstyle', 'grid_figure', 'grid_fillstyle', 'grid_gapcolor', 'grid_gid', 'grid_in_layout', 'grid_label', 'grid_linestyle', 'grid_linewidth', 'grid_marker', 'grid_markeredgecolor', 'grid_markeredgewidth', 'grid_markerfacecolor', 'grid_markerfacecoloralt', 'grid_markersize', 'grid_markevery', 'grid_mouseover', 'grid_path_effects', 'grid_picker', 'grid_pickradius', 'grid_rasterized', 'grid_sketch_params', 'grid_snap', 'grid_solid_capstyle', 'grid_solid_joinstyle', 'grid_transform', 'grid_url', 'grid_visible', 'grid_xdata', 'grid_ydata', 'grid_zorder', 'grid_aa', 'grid_c', 'grid_ds', 'grid_ls', 'grid_lw', 'grid_mec', 'grid_mew', 'grid_mfc', 'grid_mfcalt', 'grid_ms']

#Dropping the whole columns
telecom = telecom.drop('Churn Category',axis=1)
telecom = telecom.drop('Churn Reason',axis=1)
telecom = telecom.drop('Customer ID',axis=1)

#dropping the data values of "Joined" in the customer status
telecom = telecom.loc[telecom['Customer Status'] != 'Joined']

telecom.columns

Index(['Gender', 'Age', 'Married', 'Number of Dependents', 'City', 'Zip Code',
       'Latitude', 'Longitude', 'Number of Referrals', 'Tenure in Months',
       'Offer', 'Phone Service', 'Avg Monthly Long Distance Charges',
       'Multiple Lines', 'Internet Service', 'Internet Type',
       'Avg Monthly GB Download', 'Online Security', 'Online Backup',
       'Device Protection Plan', 'Premium Tech Support', 'Streaming TV',
       'Streaming Movies', 'Streaming Music', 'Unlimited Data', 'Contract',
       'Paperless Billing', 'Payment Method', 'Monthly Charge',
       'Total Charges', 'Total Refunds', 'Total Extra Data Charges',
       'Total Long Distance Charges', 'Total Revenue', 'Customer Status'],
      dtype='object')

a=telecom['Internet Service']
a.value_counts()

Internet Service
Yes    5245
No     1344
Name: count, dtype: int64

b=telecom['Internet Type']
b.value_counts()

Internet Type
Fiber Optic    2934
DSL            1537
Cable           774
Name: count, dtype: int64

telecom['Internet Type'] = telecom['Internet Type'].fillna('No Internet')
telecom['Avg Monthly GB Download'] = telecom['Avg Monthly GB Download'].fillna(0)
telecom['Online Security'] = telecom['Online Security'].fillna('No Internet')
telecom['Online Backup'] = telecom['Online Backup'].fillna('No Internet')
telecom['Device Protection Plan'] = telecom['Device Protection Plan'].fillna('No Internet')
telecom['Premium Tech Support'] = telecom['Premium Tech Support'].fillna('No Internet')
telecom['Streaming TV'] = telecom['Streaming TV'].fillna('No Internet')
telecom['Streaming Movies'] = telecom['Streaming Movies'].fillna('No Internet')
telecom['Streaming Music'] = telecom['Streaming Music'].fillna('No Internet')
telecom['Unlimited Data'] = telecom['Unlimited Data'].fillna('No Internet')

telecom.isna().sum()

Gender                                 0
Age                                    0
Married                                0
Number of Dependents                   0
City                                   0
Zip Code                               0
Latitude                               0
Longitude                              0
Number of Referrals                    0
Tenure in Months                       0
Offer                                  0
Phone Service                          0
Avg Monthly Long Distance Charges    644
Multiple Lines                       644
Internet Service                       0
Internet Type                          0
Avg Monthly GB Download                0
Online Security                        0
Online Backup                          0
Device Protection Plan                 0
Premium Tech Support                   0
Streaming TV                           0
Streaming Movies                       0
Streaming Music                        0
Unlimited Data                         0
Contract                               0
Paperless Billing                      0
Payment Method                         0
Monthly Charge                         0
Total Charges                          0
Total Refunds                          0
Total Extra Data Charges               0
Total Long Distance Charges            0
Total Revenue                          0
Customer Status                        0
dtype: int64

telecom['Phone Service'].value_counts()

Phone Service
Yes    5945
No      644
Name: count, dtype: int64

telecom['Multiple Lines'] = telecom['Multiple Lines'].fillna('No Phone Service')
telecom['Avg Monthly Long Distance Charges'] = telecom['Avg Monthly Long Distance Charges'].fillna(0)

#Dropping the duplicate values (if any)
telecom = telecom.drop_duplicates()

msno.matrix(telecom)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[32], line 1
----> 1 msno.matrix(telecom)

File C:\Anaconda\Lib\site-packages\missingno\missingno.py:72, in matrix(df, filter, n, p, sort, figsize, width_ratios, color, fontsize, labels, sparkline, inline, freq, ax)
     70 # Remove extraneous default visual elements.
     71 ax0.set_aspect('auto')
---> 72 ax0.grid(b=False)
     73 ax0.xaxis.tick_top()
     74 ax0.xaxis.set_ticks_position('none')

File C:\Anaconda\Lib\site-packages\matplotlib\axes\_base.py:3194, in _AxesBase.grid(self, visible, which, axis, **kwargs)
   3192 _api.check_in_list(['x', 'y', 'both'], axis=axis)
   3193 if axis in ['x', 'both']:
-> 3194     self.xaxis.grid(visible, which=which, **kwargs)
   3195 if axis in ['y', 'both']:
   3196     self.yaxis.grid(visible, which=which, **kwargs)

File C:\Anaconda\Lib\site-packages\matplotlib\axis.py:1698, in Axis.grid(self, visible, which, **kwargs)
   1695 if which in ['major', 'both']:
   1696     gridkw['gridOn'] = (not self._major_tick_kw['gridOn']
   1697                         if visible is None else visible)
-> 1698     self.set_tick_params(which='major', **gridkw)
   1699 self.stale = True

File C:\Anaconda\Lib\site-packages\matplotlib\axis.py:944, in Axis.set_tick_params(self, which, reset, **kwargs)
    931 """
    932 Set appearance parameters for ticks, ticklabels, and gridlines.
    933 
   (...)
    941     gridlines.
    942 """
    943 _api.check_in_list(['major', 'minor', 'both'], which=which)
--> 944 kwtrans = self._translate_tick_params(kwargs)
    946 # the kwargs are stored in self._major/minor_tick_kw so that any
    947 # future new ticks will automatically get them
    948 if reset:

File C:\Anaconda\Lib\site-packages\matplotlib\axis.py:1088, in Axis._translate_tick_params(kw, reverse)
   1086 for key in kw_:
   1087     if key not in allowed_keys:
-> 1088         raise ValueError(
   1089             "keyword %s is not recognized; valid keywords are %s"
   1090             % (key, allowed_keys))
   1091 kwtrans.update(kw_)
   1092 return kwtrans

ValueError: keyword grid_b is not recognized; valid keywords are ['size', 'width', 'color', 'tickdir', 'pad', 'labelsize', 'labelcolor', 'zorder', 'gridOn', 'tick1On', 'tick2On', 'label1On', 'label2On', 'length', 'direction', 'left', 'bottom', 'right', 'top', 'labelleft', 'labelbottom', 'labelright', 'labeltop', 'labelrotation', 'grid_agg_filter', 'grid_alpha', 'grid_animated', 'grid_antialiased', 'grid_clip_box', 'grid_clip_on', 'grid_clip_path', 'grid_color', 'grid_dash_capstyle', 'grid_dash_joinstyle', 'grid_dashes', 'grid_data', 'grid_drawstyle', 'grid_figure', 'grid_fillstyle', 'grid_gapcolor', 'grid_gid', 'grid_in_layout', 'grid_label', 'grid_linestyle', 'grid_linewidth', 'grid_marker', 'grid_markeredgecolor', 'grid_markeredgewidth', 'grid_markerfacecolor', 'grid_markerfacecoloralt', 'grid_markersize', 'grid_markevery', 'grid_mouseover', 'grid_path_effects', 'grid_picker', 'grid_pickradius', 'grid_rasterized', 'grid_sketch_params', 'grid_snap', 'grid_solid_capstyle', 'grid_solid_joinstyle', 'grid_transform', 'grid_url', 'grid_visible', 'grid_xdata', 'grid_ydata', 'grid_zorder', 'grid_aa', 'grid_c', 'grid_ds', 'grid_ls', 'grid_lw', 'grid_mec', 'grid_mew', 'grid_mfc', 'grid_mfcalt', 'grid_ms']

telecom.info()

<class 'pandas.core.frame.DataFrame'>
Index: 6589 entries, 0 to 7042
Data columns (total 35 columns):
 #   Column                             Non-Null Count  Dtype  
---  ------                             --------------  -----  
 0   Gender                             6589 non-null   object 
 1   Age                                6589 non-null   int64  
 2   Married                            6589 non-null   object 
 3   Number of Dependents               6589 non-null   int64  
 4   City                               6589 non-null   object 
 5   Zip Code                           6589 non-null   int64  
 6   Latitude                           6589 non-null   float64
 7   Longitude                          6589 non-null   float64
 8   Number of Referrals                6589 non-null   int64  
 9   Tenure in Months                   6589 non-null   int64  
 10  Offer                              6589 non-null   object 
 11  Phone Service                      6589 non-null   object 
 12  Avg Monthly Long Distance Charges  6589 non-null   float64
 13  Multiple Lines                     6589 non-null   object 
 14  Internet Service                   6589 non-null   object 
 15  Internet Type                      6589 non-null   object 
 16  Avg Monthly GB Download            6589 non-null   float64
 17  Online Security                    6589 non-null   object 
 18  Online Backup                      6589 non-null   object 
 19  Device Protection Plan             6589 non-null   object 
 20  Premium Tech Support               6589 non-null   object 
 21  Streaming TV                       6589 non-null   object 
 22  Streaming Movies                   6589 non-null   object 
 23  Streaming Music                    6589 non-null   object 
 24  Unlimited Data                     6589 non-null   object 
 25  Contract                           6589 non-null   object 
 26  Paperless Billing                  6589 non-null   object 
 27  Payment Method                     6589 non-null   object 
 28  Monthly Charge                     6589 non-null   float64
 29  Total Charges                      6589 non-null   float64
 30  Total Refunds                      6589 non-null   float64
 31  Total Extra Data Charges           6589 non-null   int64  
 32  Total Long Distance Charges        6589 non-null   float64
 33  Total Revenue                      6589 non-null   float64
 34  Customer Status                    6589 non-null   object 
dtypes: float64(9), int64(6), object(20)
memory usage: 1.8+ MB

profile = ProfileReport(telecom)

colnames=['Age','Number of Dependents','Number of Referrals','Tenure in Months','Avg Monthly Long Distance Charges',
                'Avg Monthly GB Download','Monthly Charge','Total Charges','Total Extra Data Charges','Total Long Distance Charges','Total Revenue']

fig, ax = plt.subplots(4,3, figsize = (15,15))
for i, subplot in zip(colnames, ax.flatten()):
    sns.boxplot(x = 'Customer Status', y = i , data = telecom, ax = subplot)

sns.histplot(telecom['Number of Dependents']);

sns.histplot(telecom['Number of Referrals']);

sns.histplot(telecom['Total Extra Data Charges']);

S=telecom['Total Extra Data Charges'].value_counts()
S.head()

Total Extra Data Charges
0     5905
10     113
40      61
30      52
20      48
Name: count, dtype: int64

categorical_columns = ['Gender', 'City', 'Zip Code', 'Married', 'Offer', 'Phone Service', 'Multiple Lines', 'Internet Service',
                       'Internet Type', 'Online Security', 'Online Backup', 'Device Protection Plan',
                       'Premium Tech Support', 'Streaming TV', 'Streaming Movies', 'Streaming Music',
                       'Unlimited Data', 'Contract', 'Paperless Billing', 'Payment Method', 'Customer Status']

for column in categorical_columns:
    telecom[column] = pd.Categorical(telecom[column])

telecom.dtypes

Gender                               category
Age                                     int64
Married                              category
Number of Dependents                    int64
City                                 category
Zip Code                             category
Latitude                              float64
Longitude                             float64
Number of Referrals                     int64
Tenure in Months                        int64
Offer                                category
Phone Service                        category
Avg Monthly Long Distance Charges     float64
Multiple Lines                       category
Internet Service                     category
Internet Type                        category
Avg Monthly GB Download               float64
Online Security                      category
Online Backup                        category
Device Protection Plan               category
Premium Tech Support                 category
Streaming TV                         category
Streaming Movies                     category
Streaming Music                      category
Unlimited Data                       category
Contract                             category
Paperless Billing                    category
Payment Method                       category
Monthly Charge                        float64
Total Charges                         float64
Total Refunds                         float64
Total Extra Data Charges                int64
Total Long Distance Charges           float64
Total Revenue                         float64
Customer Status                      category
dtype: object

data_telecom = telecom.copy()

df_X = telecom.drop('Customer Status', axis=1)
df_y = telecom['Customer Status']

set_config(display='diagram') # shows the pipeline graphically when printed

num_pipeline = Pipeline([
        ('imputer', SimpleImputer(strategy='median')),
        ('scaler', StandardScaler())
    ])
cat_pipeline = Pipeline([
        ('imputer', SimpleImputer(strategy='most_frequent')),
        ('cat_encoder', OneHotEncoder(sparse=False)) # returns a
        # regular matrix that can be combined easily with the data we get from
        # the numeric pipeline
    ])

SimpleImputer.get_feature_names_out = StandardScaler.get_feature_names_out  #

prep_pipeline = ColumnTransformer([
    ('num', num_pipeline, make_column_selector(dtype_include=np.number)),
    ('cat', cat_pipeline, make_column_selector(dtype_include='category'))
])

prep_pipeline

ColumnTransformer(transformers=[('num',
                                 Pipeline(steps=[('imputer',
                                                  SimpleImputer(strategy='median')),
                                                 ('scaler', StandardScaler())]),
                                 <sklearn.compose._column_transformer.make_column_selector object at 0x00000241471A7F10>),
                                ('cat',
                                 Pipeline(steps=[('imputer',
                                                  SimpleImputer(strategy='most_frequent')),
                                                 ('cat_encoder',
                                                  OneHotEncoder(sparse=False))]),
                                 <sklearn.compose._column_transformer.make_column_selector object at 0x000002413F0B0F90>)])

ColumnTransformer(transformers=[('num',
                                 Pipeline(steps=[('imputer',
                                                  SimpleImputer(strategy='median')),
                                                 ('scaler', StandardScaler())]),
                                 <sklearn.compose._column_transformer.make_column_selector object at 0x00000241471A7F10>),
                                ('cat',
                                 Pipeline(steps=[('imputer',
                                                  SimpleImputer(strategy='most_frequent')),
                                                 ('cat_encoder',
                                                  OneHotEncoder(sparse=False))]),
                                 <sklearn.compose._column_transformer.make_column_selector object at 0x000002413F0B0F90>)])

<sklearn.compose._column_transformer.make_column_selector object at 0x00000241471A7F10>

SimpleImputer(strategy='median')

StandardScaler()

<sklearn.compose._column_transformer.make_column_selector object at 0x000002413F0B0F90>

SimpleImputer(strategy='most_frequent')

OneHotEncoder(sparse=False)

processed_X = prep_pipeline.fit_transform(df_X, df_y)
df_processed_X = pd.DataFrame(processed_X,
                              columns = prep_pipeline.get_feature_names_out(),
                              index = df_X.index)
print(df_processed_X.shape)
df_processed_X.head()

(6589, 2799)

selector = SelectKBest(f_classif, k=50)
selector.fit_transform(df_processed_X, df_y)

cols = selector.get_support(indices=True)
features_df_new = df_processed_X.iloc[:,cols]

features_df_new.head()

pca = PCA(n_components=30)
pca_features = pca.fit_transform(features_df_new, df_y)
new_df = pca.transform(features_df_new)

random_forest = RandomForestClassifier()

rfe = RFE(random_forest, n_features_to_select=20)

rfe.fit(pca_features, df_y)

selected_features = rfe.support_

cols1 = rfe.get_support(indices=True)
df_3 = features_df_new.iloc[:,cols1]
df_3.columns

Index(['num__Age', 'num__Number of Dependents', 'num__Number of Referrals',
       'num__Tenure in Months', 'num__Monthly Charge', 'num__Total Charges',
       'cat__Married_Yes', 'cat__Zip Code_92122', 'cat__Offer_Offer A',
       'cat__Offer_Offer B', 'cat__Offer_Offer E', 'cat__Internet Service_Yes',
       'cat__Internet Type_DSL', 'cat__Internet Type_No Internet',
       'cat__Online Security_No', 'cat__Online Security_No Internet',
       'cat__Online Security_Yes', 'cat__Online Backup_No Internet',
       'cat__Online Backup_Yes', 'cat__Device Protection Plan_No'],
      dtype='object')

print(df_3.head())

   num__Age  num__Number of Dependents  num__Number of Referrals  \
0 -0.579600                  -0.491619                 -0.006926   
1 -0.045161                  -0.491619                 -0.668349   
2  0.192368                  -0.491619                 -0.668349   
3  1.855067                  -0.491619                 -0.337638   
4  1.676921                  -0.491619                  0.323785   

   num__Tenure in Months  num__Monthly Charge  num__Total Charges  \
0              -1.063957             0.018307           -0.811689   
1              -1.063957            -2.219753           -0.834158   
2              -1.272578             0.285202           -0.949616   
3              -0.897060             1.060162           -0.527161   
4              -1.314302             0.606762           -0.955553   

   cat__Married_Yes  cat__Zip Code_92122  cat__Offer_Offer A  \
0               1.0                  0.0                 0.0   
1               0.0                  0.0                 0.0   
2               0.0                  0.0                 0.0   
3               1.0                  0.0                 0.0   
4               1.0                  0.0                 0.0   

   cat__Offer_Offer B  cat__Offer_Offer E  cat__Internet Service_Yes  \
0                 0.0                 0.0                        1.0   
1                 0.0                 0.0                        1.0   
2                 0.0                 1.0                        1.0   
3                 0.0                 0.0                        1.0   
4                 0.0                 0.0                        1.0   

   cat__Internet Type_DSL  cat__Internet Type_No Internet  \
0                     0.0                             0.0   
1                     0.0                             0.0   
2                     0.0                             0.0   
3                     0.0                             0.0   
4                     0.0                             0.0   

   cat__Online Security_No  cat__Online Security_No Internet  \
0                      1.0                               0.0   
1                      1.0                               0.0   
2                      1.0                               0.0   
3                      1.0                               0.0   
4                      1.0                               0.0   

   cat__Online Security_Yes  cat__Online Backup_No Internet  \
0                       0.0                             0.0   
1                       0.0                             0.0   
2                       0.0                             0.0   
3                       0.0                             0.0   
4                       0.0                             0.0   

   cat__Online Backup_Yes  cat__Device Protection Plan_No  
0                     1.0                             1.0  
1                     0.0                             1.0  
2                     0.0                             0.0  
3                     1.0                             0.0  
4                     0.0                             1.0

data = df_3.values

# Perform DBSCAN clustering
dbscan = DBSCAN(eps=1, min_samples=3)
dbscan_labels = dbscan.fit_predict(data)

# Get the number of unique labels
num_unique_labels = len(np.unique(dbscan_labels))

if num_unique_labels > 1:
    # Calculate silhouette score only if there are multiple labels
    dbscan_score = silhouette_score(data, dbscan_labels)
    print("DBSCAN Silhouette Score:", dbscan_score)
else:
    print("DBSCAN: Only one label found. Unable to calculate silhouette score.")

DBSCAN Silhouette Score: -0.14134191647931937

# Perform KMeans clustering
kmeans = KMeans(n_clusters=3, random_state=24, n_init=10)
kmeans_labels = kmeans.fit_predict(data)

# Calculate silhouette score for KMeans clustering
kmeans_score = silhouette_score(data, kmeans_labels)
print("KMeans Silhouette Score:", kmeans_score)

KMeans Silhouette Score: 0.24763374148651673

num_clusters = 5
num_local = 3
max_neighborhoods = 2

# Initializing CLARANS clustering object
clarans_instance = clarans(data.tolist(), num_clusters, num_local, max_neighborhoods)

# Perform CLARANS clustering
(ticks, result) = timedcall(clarans_instance.process);
print("Execution time : ", ticks, "\n");

Execution time :  1693.922286099987

#Get the cluster labels
clst = clarans_instance.get_clusters();

#Get the best mediods of the clusters 
med = clarans_instance.get_medoids();

print("Index of clusters' points :\n",clst)
print("\nLabel class of each point :\n ",df_y)
print("\nIndex of the best medoids : ",med)

Index of clusters' points :
 [[2, 8, 22, 52, 60, 84, 89, 123, 134, 148, 156, 162, 170, 188, 203, 228, 230, 246, 257, 263, 271, 322, 362, 389, 391, 397, 410, 416, 441, 445, 453, 484, 488, 489, 493, 509, 517, 521, 526, 537, 552, 561, 574, 598, 599, 627, 645, 649, 654, 661, 690, 693, 695, 697, 748, 776, 782, 785, 796, 797, 806, 817, 847, 857, 866, 887, 888, 903, 905, 906, 945, 946, 958, 965, 975, 986, 995, 1023, 1028, 1038, 1043, 1045, 1046, 1076, 1108, 1146, 1158, 1164, 1213, 1226, 1241, 1248, 1260, 1267, 1269, 1280, 1306, 1311, 1316, 1328, 1358, 1362, 1409, 1455, 1469, 1515, 1531, 1532, 1546, 1572, 1597, 1602, 1604, 1617, 1640, 1650, 1659, 1670, 1676, 1689, 1710, 1713, 1717, 1745, 1749, 1751, 1761, 1770, 1789, 1794, 1817, 1822, 1828, 1831, 1839, 1849, 1855, 1892, 1919, 1967, 1972, 1975, 1984, 1985, 1999, 2004, 2014, 2039, 2044, 2110, 2119, 2132, 2172, 2193, 2221, 2222, 2235, 2242, 2282, 2299, 2309, 2336, 2385, 2387, 2389, 2396, 2419, 2421, 2447, 2452, 2463, 2486, 2492, 2501, 2551, 2562, 2575, 2596, 2598, 2623, 2627, 2657, 2663, 2675, 2682, 2721, 2739, 2742, 2743, 2753, 2758, 2764, 2770, 2775, 2780, 2787, 2800, 2820, 2835, 2847, 2853, 2854, 2855, 2870, 2885, 2892, 2918, 2922, 2934, 2936, 2944, 2951, 2966, 2974, 2978, 2980, 2982, 2987, 3001, 3069, 3110, 3114, 3123, 3125, 3131, 3134, 3141, 3142, 3150, 3188, 3192, 3194, 3221, 3225, 3237, 3238, 3266, 3306, 3310, 3319, 3320, 3352, 3371, 3396, 3399, 3403, 3449, 3468, 3483, 3492, 3543, 3553, 3569, 3586, 3591, 3592, 3645, 3646, 3659, 3704, 3707, 3709, 3742, 3756, 3759, 3760, 3785, 3790, 3791, 3795, 3797, 3811, 3819, 3822, 3840, 3856, 3872, 3879, 3883, 3891, 3933, 3946, 4033, 4048, 4068, 4072, 4073, 4079, 4083, 4100, 4114, 4136, 4150, 4151, 4162, 4163, 4186, 4219, 4225, 4231, 4234, 4256, 4262, 4274, 4308, 4338, 4372, 4392, 4398, 4406, 4421, 4430, 4456, 4459, 4461, 4505, 4507, 4510, 4522, 4550, 4569, 4589, 4599, 4613, 4621, 4641, 4699, 4727, 4762, 4769, 4793, 4798, 4830, 4834, 4844, 4852, 4868, 4869, 4889, 4892, 4901, 4904, 4932, 4933, 4990, 4998, 5001, 5008, 5021, 5026, 5027, 5030, 5036, 5106, 5145, 5177, 5183, 5190, 5197, 5203, 5209, 5212, 5213, 5216, 5233, 5236, 5239, 5250, 5251, 5253, 5279, 5285, 5300, 5312, 5349, 5368, 5372, 5399, 5468, 5482, 5509, 5513, 5529, 5539, 5547, 5550, 5556, 5560, 5570, 5571, 5583, 5658, 5683, 5684, 5707, 5714, 5721, 5755, 5771, 5776, 5781, 5819, 5824, 5842, 5843, 5871, 5910, 5912, 5939, 5940, 5956, 5957, 5960, 6049, 6078, 6094, 6108, 6117, 6123, 6128, 6137, 6151, 6153, 6175, 6247, 6256, 6266, 6273, 6280, 6298, 6342, 6382, 6441, 6445, 6451, 6512, 6516, 6521, 6524, 6529, 6556, 6574, 6583], [1, 12, 18, 23, 26, 32, 38, 67, 75, 86, 90, 92, 110, 114, 122, 125, 131, 165, 177, 178, 180, 182, 185, 186, 194, 195, 200, 212, 216, 240, 245, 265, 268, 270, 274, 289, 293, 311, 314, 315, 319, 325, 327, 329, 330, 332, 351, 360, 375, 380, 382, 400, 402, 404, 425, 426, 436, 457, 461, 463, 464, 478, 486, 500, 506, 507, 515, 520, 532, 535, 542, 548, 564, 571, 579, 588, 597, 606, 619, 626, 639, 641, 642, 643, 648, 656, 660, 666, 674, 678, 681, 683, 684, 700, 706, 707, 716, 728, 731, 734, 740, 742, 749, 764, 773, 775, 779, 780, 789, 794, 804, 809, 812, 813, 815, 818, 830, 854, 861, 863, 864, 870, 871, 876, 877, 881, 885, 890, 892, 894, 896, 912, 914, 916, 919, 932, 935, 937, 939, 942, 955, 977, 980, 994, 1010, 1011, 1012, 1013, 1030, 1032, 1037, 1042, 1047, 1056, 1057, 1058, 1060, 1061, 1068, 1070, 1078, 1079, 1082, 1092, 1116, 1118, 1128, 1129, 1135, 1141, 1144, 1149, 1154, 1175, 1176, 1177, 1178, 1182, 1184, 1190, 1196, 1208, 1210, 1211, 1215, 1221, 1234, 1239, 1240, 1242, 1249, 1251, 1252, 1270, 1272, 1276, 1279, 1325, 1326, 1339, 1344, 1345, 1348, 1360, 1374, 1389, 1400, 1410, 1420, 1422, 1430, 1435, 1437, 1456, 1473, 1487, 1490, 1491, 1493, 1494, 1503, 1505, 1529, 1541, 1543, 1557, 1561, 1566, 1568, 1570, 1574, 1582, 1606, 1612, 1618, 1645, 1646, 1661, 1662, 1663, 1665, 1668, 1672, 1673, 1680, 1690, 1723, 1725, 1726, 1742, 1743, 1759, 1760, 1763, 1764, 1767, 1775, 1780, 1781, 1782, 1783, 1784, 1786, 1799, 1803, 1805, 1809, 1811, 1812, 1819, 1824, 1825, 1842, 1844, 1850, 1859, 1876, 1889, 1904, 1909, 1912, 1913, 1929, 1932, 1956, 1980, 1982, 1989, 1990, 1994, 1996, 2021, 2022, 2030, 2034, 2041, 2053, 2055, 2064, 2067, 2068, 2097, 2105, 2106, 2107, 2108, 2117, 2127, 2129, 2145, 2154, 2164, 2166, 2171, 2173, 2177, 2181, 2184, 2196, 2224, 2226, 2229, 2239, 2252, 2261, 2266, 2267, 2288, 2291, 2293, 2304, 2306, 2316, 2328, 2329, 2330, 2337, 2341, 2351, 2353, 2354, 2357, 2359, 2376, 2378, 2383, 2384, 2390, 2391, 2395, 2402, 2412, 2425, 2426, 2427, 2430, 2448, 2449, 2450, 2451, 2453, 2455, 2470, 2475, 2487, 2488, 2494, 2498, 2502, 2503, 2512, 2518, 2519, 2523, 2525, 2528, 2532, 2539, 2546, 2548, 2573, 2581, 2590, 2597, 2603, 2608, 2619, 2631, 2647, 2653, 2660, 2671, 2672, 2683, 2692, 2703, 2709, 2716, 2718, 2728, 2732, 2736, 2737, 2741, 2763, 2793, 2795, 2801, 2802, 2807, 2815, 2817, 2819, 2822, 2826, 2832, 2834, 2841, 2857, 2860, 2868, 2873, 2876, 2886, 2888, 2895, 2897, 2903, 2904, 2906, 2909, 2913, 2920, 2943, 2955, 2963, 2967, 2973, 2977, 2994, 2996, 3000, 3009, 3011, 3019, 3023, 3025, 3030, 3037, 3039, 3042, 3049, 3050, 3057, 3070, 3078, 3080, 3085, 3087, 3094, 3101, 3103, 3104, 3105, 3112, 3113, 3116, 3117, 3118, 3129, 3147, 3148, 3151, 3158, 3177, 3197, 3200, 3204, 3208, 3217, 3219, 3226, 3236, 3243, 3244, 3245, 3247, 3248, 3258, 3264, 3272, 3277, 3278, 3293, 3295, 3299, 3305, 3307, 3309, 3315, 3316, 3318, 3328, 3332, 3335, 3341, 3344, 3353, 3372, 3381, 3382, 3383, 3387, 3398, 3400, 3409, 3413, 3420, 3424, 3426, 3445, 3446, 3458, 3472, 3491, 3504, 3517, 3519, 3521, 3546, 3560, 3566, 3576, 3581, 3583, 3612, 3631, 3634, 3636, 3640, 3641, 3644, 3648, 3654, 3660, 3669, 3673, 3674, 3675, 3676, 3678, 3680, 3682, 3683, 3686, 3687, 3696, 3701, 3706, 3712, 3713, 3714, 3722, 3724, 3725, 3731, 3737, 3740, 3744, 3768, 3770, 3774, 3776, 3782, 3784, 3809, 3817, 3821, 3823, 3828, 3846, 3857, 3858, 3863, 3869, 3878, 3885, 3899, 3908, 3916, 3926, 3929, 3938, 3944, 3949, 3950, 3973, 3976, 3999, 4002, 4004, 4008, 4010, 4021, 4031, 4032, 4041, 4049, 4056, 4057, 4061, 4065, 4066, 4075, 4081, 4084, 4086, 4111, 4112, 4124, 4125, 4137, 4141, 4154, 4181, 4188, 4199, 4200, 4207, 4227, 4230, 4235, 4242, 4246, 4247, 4253, 4260, 4268, 4280, 4281, 4287, 4290, 4294, 4304, 4315, 4317, 4321, 4324, 4326, 4335, 4336, 4343, 4344, 4347, 4355, 4360, 4362, 4370, 4375, 4379, 4382, 4389, 4395, 4397, 4403, 4410, 4431, 4433, 4442, 4453, 4458, 4464, 4465, 4475, 4493, 4497, 4500, 4516, 4532, 4535, 4537, 4539, 4555, 4578, 4595, 4601, 4604, 4605, 4611, 4618, 4622, 4633, 4636, 4642, 4650, 4654, 4656, 4663, 4680, 4692, 4694, 4696, 4698, 4703, 4706, 4712, 4718, 4721, 4746, 4749, 4758, 4764, 4783, 4785, 4795, 4807, 4811, 4816, 4817, 4818, 4819, 4841, 4847, 4851, 4864, 4877, 4883, 4885, 4890, 4894, 4899, 4902, 4903, 4920, 4921, 4925, 4937, 4942, 4945, 4947, 4959, 4974, 4987, 4992, 4999, 5000, 5002, 5012, 5015, 5019, 5022, 5028, 5033, 5049, 5051, 5055, 5056, 5061, 5062, 5066, 5071, 5072, 5081, 5083, 5088, 5091, 5095, 5096, 5098, 5105, 5121, 5135, 5140, 5146, 5147, 5153, 5158, 5159, 5165, 5173, 5191, 5192, 5205, 5206, 5229, 5231, 5235, 5238, 5242, 5248, 5249, 5266, 5273, 5284, 5286, 5293, 5294, 5303, 5311, 5313, 5314, 5316, 5327, 5340, 5347, 5350, 5352, 5356, 5362, 5365, 5366, 5369, 5370, 5374, 5375, 5376, 5378, 5383, 5396, 5397, 5398, 5403, 5405, 5407, 5411, 5423, 5424, 5433, 5439, 5440, 5443, 5455, 5462, 5481, 5489, 5493, 5495, 5496, 5497, 5499, 5500, 5502, 5520, 5522, 5526, 5528, 5531, 5537, 5542, 5555, 5557, 5564, 5569, 5585, 5589, 5600, 5604, 5607, 5613, 5615, 5620, 5624, 5628, 5636, 5643, 5666, 5667, 5669, 5680, 5686, 5690, 5712, 5717, 5720, 5728, 5734, 5735, 5737, 5759, 5760, 5769, 5770, 5777, 5780, 5789, 5801, 5805, 5806, 5807, 5816, 5818, 5832, 5839, 5861, 5877, 5880, 5881, 5885, 5892, 5901, 5907, 5909, 5911, 5926, 5942, 5943, 5944, 5954, 5958, 5963, 5971, 5979, 5981, 6007, 6011, 6016, 6025, 6037, 6046, 6048, 6063, 6074, 6085, 6086, 6088, 6091, 6096, 6104, 6110, 6130, 6138, 6140, 6145, 6149, 6158, 6165, 6168, 6169, 6177, 6182, 6189, 6197, 6214, 6216, 6220, 6225, 6227, 6239, 6244, 6252, 6263, 6270, 6276, 6278, 6283, 6293, 6303, 6305, 6315, 6316, 6339, 6340, 6350, 6354, 6370, 6371, 6393, 6394, 6401, 6405, 6408, 6410, 6417, 6419, 6429, 6447, 6448, 6452, 6453, 6462, 6467, 6469, 6478, 6479, 6485, 6494, 6496, 6506, 6507, 6510, 6545, 6549, 6569, 6572, 6584, 6585], [5, 25, 27, 28, 33, 36, 37, 40, 94, 101, 111, 126, 133, 144, 151, 153, 157, 159, 160, 161, 166, 173, 214, 218, 223, 233, 248, 249, 294, 301, 305, 307, 310, 312, 321, 331, 347, 361, 376, 383, 390, 423, 424, 452, 481, 492, 498, 514, 525, 534, 545, 551, 553, 578, 617, 624, 636, 658, 664, 665, 671, 675, 676, 694, 701, 713, 719, 725, 759, 778, 787, 793, 811, 828, 832, 835, 837, 841, 842, 849, 865, 872, 883, 886, 908, 911, 915, 920, 921, 922, 924, 951, 972, 976, 979, 991, 993, 1000, 1008, 1040, 1041, 1089, 1093, 1101, 1104, 1110, 1117, 1121, 1127, 1133, 1147, 1155, 1157, 1161, 1166, 1173, 1195, 1202, 1206, 1236, 1253, 1277, 1283, 1284, 1288, 1322, 1323, 1337, 1355, 1359, 1368, 1376, 1377, 1381, 1387, 1413, 1414, 1415, 1427, 1429, 1431, 1432, 1438, 1449, 1452, 1457, 1459, 1477, 1479, 1489, 1506, 1511, 1517, 1549, 1553, 1555, 1586, 1587, 1615, 1624, 1634, 1636, 1639, 1643, 1669, 1671, 1683, 1684, 1695, 1703, 1732, 1746, 1747, 1753, 1754, 1756, 1795, 1802, 1857, 1865, 1866, 1869, 1877, 1881, 1885, 1900, 1902, 1914, 1922, 1936, 1955, 1981, 1993, 2001, 2013, 2016, 2025, 2029, 2060, 2061, 2072, 2092, 2125, 2131, 2133, 2137, 2180, 2182, 2187, 2189, 2198, 2218, 2232, 2237, 2246, 2268, 2272, 2274, 2279, 2294, 2315, 2348, 2362, 2363, 2371, 2393, 2401, 2405, 2409, 2431, 2433, 2439, 2446, 2467, 2471, 2478, 2490, 2511, 2513, 2515, 2531, 2541, 2545, 2576, 2579, 2609, 2621, 2625, 2632, 2637, 2643, 2646, 2649, 2651, 2701, 2733, 2734, 2735, 2740, 2762, 2767, 2769, 2779, 2782, 2789, 2794, 2799, 2804, 2806, 2808, 2823, 2831, 2840, 2845, 2862, 2871, 2872, 2882, 2884, 2899, 2907, 2910, 2911, 2917, 2925, 2937, 2939, 2941, 2969, 2989, 2990, 3018, 3021, 3022, 3028, 3054, 3060, 3064, 3077, 3079, 3092, 3100, 3115, 3132, 3135, 3136, 3139, 3153, 3159, 3160, 3171, 3205, 3207, 3209, 3210, 3216, 3220, 3254, 3261, 3267, 3273, 3283, 3287, 3290, 3297, 3300, 3331, 3336, 3343, 3355, 3392, 3406, 3418, 3421, 3434, 3435, 3453, 3473, 3480, 3484, 3505, 3516, 3531, 3541, 3547, 3549, 3563, 3587, 3607, 3608, 3618, 3638, 3649, 3655, 3662, 3665, 3685, 3689, 3703, 3720, 3747, 3769, 3775, 3777, 3818, 3831, 3839, 3849, 3854, 3859, 3865, 3871, 3876, 3884, 3888, 3890, 3901, 3915, 3918, 3928, 3931, 3937, 3953, 3955, 3957, 3990, 3998, 4012, 4022, 4025, 4046, 4064, 4074, 4101, 4109, 4130, 4132, 4140, 4143, 4168, 4169, 4204, 4224, 4265, 4279, 4291, 4298, 4306, 4311, 4314, 4318, 4322, 4328, 4331, 4333, 4341, 4367, 4374, 4383, 4391, 4396, 4416, 4419, 4428, 4439, 4448, 4452, 4498, 4499, 4504, 4508, 4515, 4517, 4525, 4528, 4548, 4552, 4554, 4557, 4561, 4567, 4580, 4615, 4626, 4627, 4635, 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Label class of each point :
  0        Stayed
1        Stayed
2       Churned
3       Churned
4       Churned
         ...   
7037    Churned
7038     Stayed
7039    Churned
7041     Stayed
7042     Stayed
Name: Customer Status, Length: 6589, dtype: category
Categories (2, object): ['Churned', 'Stayed']

Index of the best medoids :  [1260, 4154, 3100, 2830, 6199]

# Applying PCA for dimensionality reduction
pca = PCA(n_components=2)
data_pca = pca.fit_transform(data)

# Creating a scatter plot for DBSCAN clustering results
plt.figure(figsize=(8, 6))
plt.scatter(data_pca[:, 0], data_pca[:, 1], c=dbscan_labels, cmap='rainbow', s=50)
plt.title("DBSCAN Clustering")
plt.xlabel("Principal Component 1")
plt.ylabel("Principal Component 2")
plt.colorbar(label="Cluster Labels")
plt.show()

# Applying PCA for dimensionality reduction
pca = PCA(n_components=2)
data_pca = pca.fit_transform(data)

# Creating a scatter plot for KMeans clustering results
plt.figure(figsize=(8, 6))
plt.scatter(data_pca[:, 0], data_pca[:, 1], c=kmeans_labels, cmap='rainbow', s=50)
plt.title("KMeans Clustering")
plt.xlabel("Principal Component 1")
plt.ylabel("Principal Component 2")
plt.colorbar(label="Cluster Labels")
plt.show()

# Retrieving the CLARANS cluster assignments
clarans_clusters = clarans_instance.get_clusters()

# Creating a scatter plot for visualizing the clusters
plt.figure(figsize=(6, 4))

for cluster_idx, cluster in enumerate(clarans_clusters):
    cluster_points = data[cluster]
    plt.scatter(cluster_points[:, 0], cluster_points[:, 1], label=f'Cluster {cluster_idx + 1}')

plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.title('CLARANS Clustering Results')
plt.legend()
plt.show()

#Gathering CLARANS Clustering labels for modeling data
num_clusters = len(np.unique(np.concatenate(clarans_clusters)))
cluster_feature = np.zeros((len(data), num_clusters))

for cluster_idx, cluster in enumerate(clarans_clusters):
    cluster_feature[cluster, cluster_idx] = 1

data_with_cluster = np.hstack((data, cluster_feature))

X = data_with_cluster
y = df_y

# Splitting data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

df_X_test = pd.DataFrame(X_test)
X_test_indices = df_X_test.index

customerID = originalData.loc[X_test_indices, 'Customer ID']

# Encoding target labels
label_encoder = LabelEncoder()
y_train_encoded = label_encoder.fit_transform(y_train)
y_test_encoded = label_encoder.transform(y_test)
num_classes = len(label_encoder.classes_)

#Reshaping the data to be compatible with neural network models
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)

# SWISH activation function Definition
def swish(x):
    return x * tf.sigmoid(x)

# MISH activation function Definition
def mish(x):
    return x * tf.math.tanh(tf.math.softplus(x))

# APTx activation function Definition
def aptx(x):
    return x * tf.math.tanh(tf.math.softplus(x) + 1)

def build_ffnn_model(activation_function):
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(64, activation=activation_function, input_shape=(X_train.shape[1],)),
        tf.keras.layers.Dense(32, activation=activation_function),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])

    return model

def build_cnn_model(activation_function):
    model = tf.keras.Sequential([
        tf.keras.layers.Conv1D(16, 3, activation=activation_function, padding='same', input_shape=(X_train.shape[1], 1)),
        tf.keras.layers.MaxPooling1D(2),
        tf.keras.layers.Conv1D(32, 3, activation=activation_function, padding='same'),
        tf.keras.layers.MaxPooling1D(2),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(128, activation=activation_function),
        tf.keras.layers.Dense(10, activation='softmax')
    ])

    return model

def compileandtrainmodel(model_name, model_type, epochs, batch_size):
    print(f"Building Model: {model_name}")
    model_type.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
    model_type.fit(X_train, y_train_encoded, validation_data=(X_test, y_test_encoded), epochs=epochs, batch_size=batch_size)
    loss, accuracy = model_type.evaluate(X_test, y_test_encoded)
    print(f"Model Name: {model_name} ==> Test Loss: {loss:.4f}, Test Accuracy: {accuracy:.4f}")

def metricscalculation(modelname, model, X_val, y_val):
    # Predicted labels for the model
    model_predictions = model.predict(X_val)
    model_preditcion_classes = np.argmax(model_predictions, axis=1)

    # Confusion matrix for the model
    model_confusion_matrix = confusion_matrix(y_val, model_preditcion_classes)
    print(f"{modelname} Confusion Matrix:")
    print(model_confusion_matrix)

    # Extracting values from Confusion Matrix
    TP = model_confusion_matrix[1, 1]
    FP = model_confusion_matrix[0, 1]
    TN = model_confusion_matrix[0, 0]
    FN = model_confusion_matrix[1, 0]
    
    # Calculation various metrics for the model
    model_precision = precision_score(y_val, model_preditcion_classes)
    model_recall = recall_score(y_val, model_preditcion_classes)
    model_f1 = f1_score(y_val, model_preditcion_classes)
    
    sensitivity = TP / (TP + FN)
    specificity = TN / (TN + FP)

    print("True Positives:", TP)
    print("False Positives:", FP)
    print("True Negatives:", TN)
    print("False Negatives:", FN)
    
    modelmetrics_df = pd.DataFrame({
    "Metric": ["Precision", "Recall", "F1 Score", "Sensitivity", "Specificity"],
    modelname: [model_precision, model_recall, model_f1, sensitivity, specificity]
    })

    # Print the metrics table
    print(modelmetrics_df)

import plotly.graph_objects as go
import pandas as pd

def create_table_with_formatting(actual_values, predicted_values, customer_ids):
    # Create DataFrame
    data = {'Customer ID': customer_ids, 'True Values': actual_values, 'Predicted Values': predicted_values}
    df = pd.DataFrame(data)
    df.insert(0, 'S.No', range(1, 1 + len(df)))

    # Map 1 to 'Not Churn' and 0 to 'Churn' in 'True Values' and 'Predicted Values' columns
    df['True Values'] = df['True Values'].map({1: 'Not Churn', 0: 'Churn'})
    df['Predicted Values'] = df['Predicted Values'].map({1: 'Not Churn', 0: 'Churn'})

    # Add a correctness column
    df['Correct Prediction'] = df['True Values'] == df['Predicted Values']

    # Create a table with Plotly Graph Objects
    fig = go.Figure(data=[go.Table(
        header=dict(values=df.columns),
        cells=dict(values=df.transpose().values),
    )])

    # Conditional formatting for false predictions
    color_map = {True: 'palegreen', False: 'lightsalmon'}
    cell_colors = [[color_map[correct] for correct in df['Correct Prediction']]]

    # Update table layout with conditional formatting
    fig.update_traces(cells=dict(fill=dict(color=cell_colors)))

    # Customize layout
    fig.update_layout(
        title_text='Table: Predictions vs True Values',
        autosize=True,
        width=700,  # Total width of the table
        margin=dict(l=0, r=0, b=0, t=40),  # Adjust margins as needed
    )

    return fig

model_name = "Feed Forward Neural Network with ReLU Activation Function"
ffnn_model_relu = build_ffnn_model('relu')

compileandtrainmodel(model_name, ffnn_model_relu, 10, 40)

WARNING:tensorflow:From C:\Anaconda\Lib\site-packages\keras\src\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

Building Model: Feed Forward Neural Network with ReLU Activation Function
WARNING:tensorflow:From C:\Anaconda\Lib\site-packages\keras\src\optimizers\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.

Epoch 1/10
WARNING:tensorflow:From C:\Anaconda\Lib\site-packages\keras\src\utils\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.

WARNING:tensorflow:From C:\Anaconda\Lib\site-packages\keras\src\engine\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.

132/132 [==============================] - 3s 9ms/step - loss: 0.4393 - accuracy: 0.7962 - val_loss: 0.3823 - val_accuracy: 0.8118
Epoch 2/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3720 - accuracy: 0.8275 - val_loss: 0.3545 - val_accuracy: 0.8316
Epoch 3/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3634 - accuracy: 0.8291 - val_loss: 0.3569 - val_accuracy: 0.8300
Epoch 4/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3585 - accuracy: 0.8349 - val_loss: 0.3561 - val_accuracy: 0.8323
Epoch 5/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3533 - accuracy: 0.8376 - val_loss: 0.3468 - val_accuracy: 0.8392
Epoch 6/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3497 - accuracy: 0.8412 - val_loss: 0.3440 - val_accuracy: 0.8384
Epoch 7/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3472 - accuracy: 0.8387 - val_loss: 0.3448 - val_accuracy: 0.8429
Epoch 8/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3473 - accuracy: 0.8427 - val_loss: 0.3449 - val_accuracy: 0.8361
Epoch 9/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3396 - accuracy: 0.8433 - val_loss: 0.3470 - val_accuracy: 0.8293
Epoch 10/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3427 - accuracy: 0.8433 - val_loss: 0.3418 - val_accuracy: 0.8361
42/42 [==============================] - 0s 3ms/step - loss: 0.3418 - accuracy: 0.8361
Model Name: Feed Forward Neural Network with ReLU Activation Function ==> Test Loss: 0.3418, Test Accuracy: 0.8361

metricscalculation(model_name, ffnn_model_relu, X_test, y_test_encoded)

42/42 [==============================] - 0s 3ms/step
Feed Forward Neural Network with ReLU Activation Function Confusion Matrix:
[[225 158]
 [ 58 877]]
True Positives: 877
False Positives: 158
True Negatives: 225
False Negatives: 58
        Metric  Feed Forward Neural Network with ReLU Activation Function
0    Precision                                           0.847343        
1       Recall                                           0.937968        
2     F1 Score                                           0.890355        
3  Sensitivity                                           0.937968        
4  Specificity                                           0.587467

## FFNN ReLU
fig = create_table_with_formatting(y_test_encoded, np.argmax(ffnn_model_relu.predict(X_test), axis=1), customerID)
fig.show()

42/42 [==============================] - 0s 4ms/step

model_name = "Feed Forward Neural Network with SWISH Activation Function"
ffnn_model_swish = build_ffnn_model(swish)

compileandtrainmodel(model_name, ffnn_model_swish, 10, 40)

Building Model: Feed Forward Neural Network with SWISH Activation Function
Epoch 1/10
132/132 [==============================] - 2s 8ms/step - loss: 0.4442 - accuracy: 0.7868 - val_loss: 0.3877 - val_accuracy: 0.8171
Epoch 2/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3791 - accuracy: 0.8219 - val_loss: 0.3711 - val_accuracy: 0.8300
Epoch 3/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3720 - accuracy: 0.8275 - val_loss: 0.3626 - val_accuracy: 0.8308
Epoch 4/10
132/132 [==============================] - 1s 7ms/step - loss: 0.3684 - accuracy: 0.8262 - val_loss: 0.3684 - val_accuracy: 0.8308
Epoch 5/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3678 - accuracy: 0.8283 - val_loss: 0.3607 - val_accuracy: 0.8293
Epoch 6/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3656 - accuracy: 0.8285 - val_loss: 0.3583 - val_accuracy: 0.8338
Epoch 7/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3653 - accuracy: 0.8312 - val_loss: 0.3599 - val_accuracy: 0.8247
Epoch 8/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3637 - accuracy: 0.8321 - val_loss: 0.3601 - val_accuracy: 0.8308
Epoch 9/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3641 - accuracy: 0.8327 - val_loss: 0.3594 - val_accuracy: 0.8285
Epoch 10/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3604 - accuracy: 0.8313 - val_loss: 0.3544 - val_accuracy: 0.8331
42/42 [==============================] - 0s 3ms/step - loss: 0.3544 - accuracy: 0.8331
Model Name: Feed Forward Neural Network with SWISH Activation Function ==> Test Loss: 0.3544, Test Accuracy: 0.8331

metricscalculation(model_name, ffnn_model_swish, X_test, y_test_encoded)

42/42 [==============================] - 0s 3ms/step
Feed Forward Neural Network with SWISH Activation Function Confusion Matrix:
[[248 135]
 [ 85 850]]
True Positives: 850
False Positives: 135
True Negatives: 248
False Negatives: 85
        Metric  Feed Forward Neural Network with SWISH Activation Function
0    Precision                                           0.862944         
1       Recall                                           0.909091         
2     F1 Score                                           0.885417         
3  Sensitivity                                           0.909091         
4  Specificity                                           0.647520

## FFNN SWISH
fig = create_table_with_formatting(y_test_encoded, np.argmax(ffnn_model_swish.predict(X_test), axis=1), customerID)
fig.show()

42/42 [==============================] - 0s 3ms/step

model_name = "Feed Forward Neural Network with MISH Activation Function"
ffnn_model_mish = build_ffnn_model(mish)

compileandtrainmodel(model_name, ffnn_model_mish, 10, 40)

Building Model: Feed Forward Neural Network with MISH Activation Function
Epoch 1/10
132/132 [==============================] - 2s 8ms/step - loss: 0.4290 - accuracy: 0.8019 - val_loss: 0.3871 - val_accuracy: 0.8179
Epoch 2/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3797 - accuracy: 0.8228 - val_loss: 0.3692 - val_accuracy: 0.8270
Epoch 3/10
132/132 [==============================] - 1s 7ms/step - loss: 0.3722 - accuracy: 0.8272 - val_loss: 0.3636 - val_accuracy: 0.8285
Epoch 4/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3687 - accuracy: 0.8268 - val_loss: 0.3603 - val_accuracy: 0.8278
Epoch 5/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3689 - accuracy: 0.8285 - val_loss: 0.3585 - val_accuracy: 0.8331
Epoch 6/10
132/132 [==============================] - 1s 7ms/step - loss: 0.3662 - accuracy: 0.8304 - val_loss: 0.3582 - val_accuracy: 0.8285
Epoch 7/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3660 - accuracy: 0.8329 - val_loss: 0.3587 - val_accuracy: 0.8323
Epoch 8/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3646 - accuracy: 0.8332 - val_loss: 0.3569 - val_accuracy: 0.8323
Epoch 9/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3628 - accuracy: 0.8293 - val_loss: 0.3557 - val_accuracy: 0.8346
Epoch 10/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3611 - accuracy: 0.8321 - val_loss: 0.3616 - val_accuracy: 0.8255
42/42 [==============================] - 0s 3ms/step - loss: 0.3616 - accuracy: 0.8255
Model Name: Feed Forward Neural Network with MISH Activation Function ==> Test Loss: 0.3616, Test Accuracy: 0.8255

metricscalculation(model_name, ffnn_model_mish, X_test, y_test_encoded)

42/42 [==============================] - 0s 3ms/step
Feed Forward Neural Network with MISH Activation Function Confusion Matrix:
[[212 171]
 [ 59 876]]
True Positives: 876
False Positives: 171
True Negatives: 212
False Negatives: 59
        Metric  Feed Forward Neural Network with MISH Activation Function
0    Precision                                           0.836676        
1       Recall                                           0.936898        
2     F1 Score                                           0.883956        
3  Sensitivity                                           0.936898        
4  Specificity                                           0.553525

## FFNN MISH
fig = create_table_with_formatting(y_test_encoded, np.argmax(ffnn_model_mish.predict(X_test), axis=1), customerID)
fig.show()

42/42 [==============================] - 0s 2ms/step

model_name = "Feed Forward Neural Network with APTx Activation Function"
ffnn_model_aptx = build_ffnn_model(aptx)

compileandtrainmodel(model_name, ffnn_model_aptx, 10, 40)

Building Model: Feed Forward Neural Network with APTx Activation Function
Epoch 1/10
132/132 [==============================] - 2s 8ms/step - loss: 0.4132 - accuracy: 0.8025 - val_loss: 0.3794 - val_accuracy: 0.8209
Epoch 2/10
132/132 [==============================] - 1s 5ms/step - loss: 0.3769 - accuracy: 0.8215 - val_loss: 0.3692 - val_accuracy: 0.8255
Epoch 3/10
132/132 [==============================] - 1s 5ms/step - loss: 0.3707 - accuracy: 0.8313 - val_loss: 0.3613 - val_accuracy: 0.8293
Epoch 4/10
132/132 [==============================] - 1s 5ms/step - loss: 0.3718 - accuracy: 0.8258 - val_loss: 0.3632 - val_accuracy: 0.8247
Epoch 5/10
132/132 [==============================] - 1s 5ms/step - loss: 0.3701 - accuracy: 0.8317 - val_loss: 0.3611 - val_accuracy: 0.8278
Epoch 6/10
132/132 [==============================] - 1s 6ms/step - loss: 0.3698 - accuracy: 0.8283 - val_loss: 0.3661 - val_accuracy: 0.8263
Epoch 7/10
132/132 [==============================] - 1s 5ms/step - loss: 0.3682 - accuracy: 0.8306 - val_loss: 0.3604 - val_accuracy: 0.8278
Epoch 8/10
132/132 [==============================] - 1s 5ms/step - loss: 0.3687 - accuracy: 0.8308 - val_loss: 0.3597 - val_accuracy: 0.8270
Epoch 9/10
132/132 [==============================] - 1s 5ms/step - loss: 0.3685 - accuracy: 0.8300 - val_loss: 0.3590 - val_accuracy: 0.8308
Epoch 10/10
132/132 [==============================] - 1s 5ms/step - loss: 0.3673 - accuracy: 0.8330 - val_loss: 0.3626 - val_accuracy: 0.8278
42/42 [==============================] - 0s 2ms/step - loss: 0.3626 - accuracy: 0.8278
Model Name: Feed Forward Neural Network with APTx Activation Function ==> Test Loss: 0.3626, Test Accuracy: 0.8278

metricscalculation(model_name, ffnn_model_aptx, X_test, y_test_encoded)

42/42 [==============================] - 0s 2ms/step
Feed Forward Neural Network with APTx Activation Function Confusion Matrix:
[[244 139]
 [ 88 847]]
True Positives: 847
False Positives: 139
True Negatives: 244
False Negatives: 88
        Metric  Feed Forward Neural Network with APTx Activation Function
0    Precision                                           0.859026        
1       Recall                                           0.905882        
2     F1 Score                                           0.881832        
3  Sensitivity                                           0.905882        
4  Specificity                                           0.637076

## FFNN APTx
fig = create_table_with_formatting(y_test_encoded, np.argmax(ffnn_model_aptx.predict(X_test), axis=1), customerID)
fig.show()

42/42 [==============================] - 0s 4ms/step

model_name = "Convolutional Neural Network with ReLU Activation Function"
cnn_model_relu = build_ffnn_model('relu')

compileandtrainmodel(model_name, cnn_model_relu, 10, 32)

Building Model: Convolutional Neural Network with ReLU Activation Function
Epoch 1/10
165/165 [==============================] - 2s 8ms/step - loss: 0.4529 - accuracy: 0.8021 - val_loss: 0.3833 - val_accuracy: 0.8209
Epoch 2/10
165/165 [==============================] - 1s 6ms/step - loss: 0.3775 - accuracy: 0.8247 - val_loss: 0.3651 - val_accuracy: 0.8270
Epoch 3/10
165/165 [==============================] - 1s 6ms/step - loss: 0.3663 - accuracy: 0.8293 - val_loss: 0.3524 - val_accuracy: 0.8323
Epoch 4/10
165/165 [==============================] - 1s 6ms/step - loss: 0.3572 - accuracy: 0.8359 - val_loss: 0.3474 - val_accuracy: 0.8293
Epoch 5/10
165/165 [==============================] - 1s 6ms/step - loss: 0.3518 - accuracy: 0.8372 - val_loss: 0.3452 - val_accuracy: 0.8437
Epoch 6/10
165/165 [==============================] - 1s 6ms/step - loss: 0.3481 - accuracy: 0.8420 - val_loss: 0.3392 - val_accuracy: 0.8414
Epoch 7/10
165/165 [==============================] - 1s 6ms/step - loss: 0.3444 - accuracy: 0.8406 - val_loss: 0.3419 - val_accuracy: 0.8331
Epoch 8/10
165/165 [==============================] - 1s 6ms/step - loss: 0.3383 - accuracy: 0.8461 - val_loss: 0.3463 - val_accuracy: 0.8323
Epoch 9/10
165/165 [==============================] - 1s 6ms/step - loss: 0.3375 - accuracy: 0.8448 - val_loss: 0.3367 - val_accuracy: 0.8376
Epoch 10/10
165/165 [==============================] - 1s 6ms/step - loss: 0.3341 - accuracy: 0.8478 - val_loss: 0.3355 - val_accuracy: 0.8437
42/42 [==============================] - 0s 3ms/step - loss: 0.3355 - accuracy: 0.8437
Model Name: Convolutional Neural Network with ReLU Activation Function ==> Test Loss: 0.3355, Test Accuracy: 0.8437

metricscalculation(model_name, cnn_model_relu, X_test, y_test_encoded)

42/42 [==============================] - 0s 3ms/step
Convolutional Neural Network with ReLU Activation Function Confusion Matrix:
[[252 131]
 [ 75 860]]
True Positives: 860
False Positives: 131
True Negatives: 252
False Negatives: 75
        Metric  Convolutional Neural Network with ReLU Activation Function
0    Precision                                           0.867810         
1       Recall                                           0.919786         
2     F1 Score                                           0.893043         
3  Sensitivity                                           0.919786         
4  Specificity                                           0.657963

## CNN ReLU
fig = create_table_with_formatting(y_test_encoded, np.argmax(cnn_model_relu.predict(X_test), axis=1), customerID)
fig.show()

42/42 [==============================] - 0s 3ms/step

model_name = "Convolutional Neural Network with SWISH Activation Function"
cnn_model_swish = build_ffnn_model(swish)

compileandtrainmodel(model_name, cnn_model_swish, 10, 32)

Building Model: Convolutional Neural Network with SWISH Activation Function
Epoch 1/10
165/165 [==============================] - 2s 7ms/step - loss: 0.4313 - accuracy: 0.8012 - val_loss: 0.3817 - val_accuracy: 0.8164
Epoch 2/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3778 - accuracy: 0.8256 - val_loss: 0.3655 - val_accuracy: 0.8232
Epoch 3/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3716 - accuracy: 0.8304 - val_loss: 0.3665 - val_accuracy: 0.8225
Epoch 4/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3696 - accuracy: 0.8274 - val_loss: 0.3629 - val_accuracy: 0.8240
Epoch 5/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3694 - accuracy: 0.8275 - val_loss: 0.3665 - val_accuracy: 0.8202
Epoch 6/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3658 - accuracy: 0.8287 - val_loss: 0.3592 - val_accuracy: 0.8369
Epoch 7/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3664 - accuracy: 0.8315 - val_loss: 0.3601 - val_accuracy: 0.8354
Epoch 8/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3636 - accuracy: 0.8329 - val_loss: 0.3576 - val_accuracy: 0.8293
Epoch 9/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3624 - accuracy: 0.8327 - val_loss: 0.3569 - val_accuracy: 0.8338
Epoch 10/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3588 - accuracy: 0.8374 - val_loss: 0.3532 - val_accuracy: 0.8361
42/42 [==============================] - 0s 2ms/step - loss: 0.3532 - accuracy: 0.8361
Model Name: Convolutional Neural Network with SWISH Activation Function ==> Test Loss: 0.3532, Test Accuracy: 0.8361

metricscalculation(model_name, cnn_model_swish, X_test, y_test_encoded)

42/42 [==============================] - 0s 3ms/step
Convolutional Neural Network with SWISH Activation Function Confusion Matrix:
[[241 142]
 [ 74 861]]
True Positives: 861
False Positives: 142
True Negatives: 241
False Negatives: 74
        Metric  Convolutional Neural Network with SWISH Activation Function
0    Precision                                           0.858425          
1       Recall                                           0.920856          
2     F1 Score                                           0.888545          
3  Sensitivity                                           0.920856          
4  Specificity                                           0.629243

## CNN SWISH
fig = create_table_with_formatting(y_test_encoded, np.argmax(cnn_model_swish.predict(X_test), axis=1), customerID)
fig.show()

42/42 [==============================] - 0s 4ms/step

model_name = "Convolutional Neural Network with MISH Activation Function"
cnn_model_mish = build_ffnn_model(mish)

compileandtrainmodel(model_name, cnn_model_mish, 10, 32)

Building Model: Convolutional Neural Network with MISH Activation Function
Epoch 1/10
165/165 [==============================] - 2s 6ms/step - loss: 0.4266 - accuracy: 0.7955 - val_loss: 0.3785 - val_accuracy: 0.8217
Epoch 2/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3770 - accuracy: 0.8247 - val_loss: 0.3687 - val_accuracy: 0.8285
Epoch 3/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3709 - accuracy: 0.8238 - val_loss: 0.3626 - val_accuracy: 0.8285
Epoch 4/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3700 - accuracy: 0.8281 - val_loss: 0.3611 - val_accuracy: 0.8278
Epoch 5/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3682 - accuracy: 0.8294 - val_loss: 0.3596 - val_accuracy: 0.8316
Epoch 6/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3660 - accuracy: 0.8293 - val_loss: 0.3637 - val_accuracy: 0.8209
Epoch 7/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3644 - accuracy: 0.8298 - val_loss: 0.3550 - val_accuracy: 0.8293
Epoch 8/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3628 - accuracy: 0.8334 - val_loss: 0.3556 - val_accuracy: 0.8323
Epoch 9/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3612 - accuracy: 0.8321 - val_loss: 0.3557 - val_accuracy: 0.8346
Epoch 10/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3605 - accuracy: 0.8327 - val_loss: 0.3502 - val_accuracy: 0.8331
42/42 [==============================] - 0s 2ms/step - loss: 0.3502 - accuracy: 0.8331
Model Name: Convolutional Neural Network with MISH Activation Function ==> Test Loss: 0.3502, Test Accuracy: 0.8331

metricscalculation(model_name, cnn_model_mish, X_test, y_test_encoded)

42/42 [==============================] - 0s 3ms/step
Convolutional Neural Network with MISH Activation Function Confusion Matrix:
[[242 141]
 [ 79 856]]
True Positives: 856
False Positives: 141
True Negatives: 242
False Negatives: 79
        Metric  Convolutional Neural Network with MISH Activation Function
0    Precision                                           0.858576         
1       Recall                                           0.915508         
2     F1 Score                                           0.886128         
3  Sensitivity                                           0.915508         
4  Specificity                                           0.631854

## CNN MISH
fig = create_table_with_formatting(y_test_encoded, np.argmax(cnn_model_mish.predict(X_test), axis=1), customerID)
fig.show()

42/42 [==============================] - 0s 4ms/step

model_name = "Convolutional Neural Network with APTx Activation Function"
cnn_model_aptx = build_ffnn_model(aptx)

compileandtrainmodel(model_name, cnn_model_aptx, 10, 32)

Building Model: Convolutional Neural Network with APTx Activation Function
Epoch 1/10
165/165 [==============================] - 2s 7ms/step - loss: 0.4140 - accuracy: 0.8029 - val_loss: 0.3791 - val_accuracy: 0.8217
Epoch 2/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3763 - accuracy: 0.8234 - val_loss: 0.3790 - val_accuracy: 0.8187
Epoch 3/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3748 - accuracy: 0.8238 - val_loss: 0.3704 - val_accuracy: 0.8209
Epoch 4/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3715 - accuracy: 0.8281 - val_loss: 0.3652 - val_accuracy: 0.8285
Epoch 5/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3699 - accuracy: 0.8279 - val_loss: 0.3609 - val_accuracy: 0.8270
Epoch 6/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3699 - accuracy: 0.8264 - val_loss: 0.3722 - val_accuracy: 0.8232
Epoch 7/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3701 - accuracy: 0.8255 - val_loss: 0.3624 - val_accuracy: 0.8202
Epoch 8/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3697 - accuracy: 0.8289 - val_loss: 0.3702 - val_accuracy: 0.8202
Epoch 9/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3691 - accuracy: 0.8258 - val_loss: 0.3629 - val_accuracy: 0.8316
Epoch 10/10
165/165 [==============================] - 1s 5ms/step - loss: 0.3676 - accuracy: 0.8287 - val_loss: 0.3648 - val_accuracy: 0.8270
42/42 [==============================] - 0s 2ms/step - loss: 0.3648 - accuracy: 0.8270
Model Name: Convolutional Neural Network with APTx Activation Function ==> Test Loss: 0.3648, Test Accuracy: 0.8270

metricscalculation(model_name, cnn_model_aptx, X_test, y_test_encoded)

42/42 [==============================] - 0s 2ms/step
Convolutional Neural Network with APTx Activation Function Confusion Matrix:
[[218 165]
 [ 63 872]]
True Positives: 872
False Positives: 165
True Negatives: 218
False Negatives: 63
        Metric  Convolutional Neural Network with APTx Activation Function
0    Precision                                           0.840887         
1       Recall                                           0.932620         
2     F1 Score                                           0.884381         
3  Sensitivity                                           0.932620         
4  Specificity                                           0.569191

## CNN APTx
fig = create_table_with_formatting(y_test_encoded, np.argmax(cnn_model_aptx.predict(X_test), axis=1), customerID)
fig.show()

42/42 [==============================] - 0s 4ms/step

# Combining the outputs of CNN and FFNN Models
combinedInput = concatenate([cnn_model_swish.output, ffnn_model_swish.output])

# Add Fully Connected layers for Ensemble Model
x = Dense(4, activation=mish)(combinedInput)
x = Dense(1, activation="sigmoid")(x)

# Creating the Ensemble Model
ensemble_model = Model(inputs=[cnn_model_swish.input, ffnn_model_swish.input], outputs=x)

# Compiling the Ensemble Model
ensemble_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Training the Ensemble Model
ensemble_model.fit([X_train, X_train], y_train_encoded, validation_data = ([X_test, X_test], y_test_encoded), epochs=10, batch_size=32, verbose=1)

# Making predictions with the Ensemble Model
ensemble_predictions = ensemble_model.predict([X_test, X_test])
ensemble_classes = np.round(ensemble_predictions)

# Calculating the accuracy of Ensemble Model
accuracy = accuracy_score(y_test_encoded, ensemble_classes)
print(f"Ensemble Accuracy: {accuracy:.4f}")

Epoch 1/10
165/165 [==============================] - 5s 11ms/step - loss: 0.5377 - accuracy: 0.8245 - val_loss: 0.4797 - val_accuracy: 0.8171
Epoch 2/10
165/165 [==============================] - 1s 9ms/step - loss: 0.4374 - accuracy: 0.8317 - val_loss: 0.4239 - val_accuracy: 0.8225
Epoch 3/10
165/165 [==============================] - 2s 9ms/step - loss: 0.3972 - accuracy: 0.8348 - val_loss: 0.3843 - val_accuracy: 0.8346
Epoch 4/10
165/165 [==============================] - 2s 9ms/step - loss: 0.3760 - accuracy: 0.8380 - val_loss: 0.3685 - val_accuracy: 0.8414
Epoch 5/10
165/165 [==============================] - 1s 9ms/step - loss: 0.3657 - accuracy: 0.8389 - val_loss: 0.3635 - val_accuracy: 0.8369
Epoch 6/10
165/165 [==============================] - 1s 9ms/step - loss: 0.3583 - accuracy: 0.8393 - val_loss: 0.3561 - val_accuracy: 0.8414
Epoch 7/10
165/165 [==============================] - 1s 8ms/step - loss: 0.3550 - accuracy: 0.8408 - val_loss: 0.3566 - val_accuracy: 0.8369
Epoch 8/10
165/165 [==============================] - 1s 9ms/step - loss: 0.3524 - accuracy: 0.8406 - val_loss: 0.3474 - val_accuracy: 0.8505
Epoch 9/10
165/165 [==============================] - 1s 8ms/step - loss: 0.3493 - accuracy: 0.8433 - val_loss: 0.3488 - val_accuracy: 0.8323
Epoch 10/10
165/165 [==============================] - 1s 8ms/step - loss: 0.3450 - accuracy: 0.8425 - val_loss: 0.3465 - val_accuracy: 0.8376
42/42 [==============================] - 0s 3ms/step
Ensemble Accuracy: 0.8376

## Ensemble Predictions
fig = create_table_with_formatting(y_test_encoded, ensemble_classes[0][0], customerID)
fig.show()

# Reshaping ensemble_predictions to be a 2-dimensional matrix
ensemble_predictions_reshaped = np.reshape(ensemble_predictions, (len(ensemble_predictions), -1))

# Defining the hyperparameter grid
param_grid = {
    'n_estimators': [450, 460, 470, 480],
    'max_depth': [10, 11, 12],
    'learning_rate': [0.3, 0.4, 0.5]
}

# Initializing the XGBoost classifier
xgb_classifier = XGBClassifier()

# Initializing GridSearchCV with cross-validation
grid_search = GridSearchCV(estimator=xgb_classifier, param_grid=param_grid, scoring='accuracy', cv=3)

# Fitting the grid search to the data
grid_search.fit(ensemble_predictions_reshaped, y_test_encoded)

# Obtaining the best hyperparameters and model
best_params = grid_search.best_params_
best_model = grid_search.best_estimator_

# Printing the best parameters
print("Best Hyperparameters:", best_params)

# Storing the best parameters
xgb_n_estimators = best_params['n_estimators']
xgb_max_depth = best_params['max_depth']
xgb_learning_rate = best_params['learning_rate']

# Making predictions using the best model
best_predictions = best_model.predict(ensemble_predictions_reshaped)

# Calculating accuracy using the true labels
accuracy = accuracy_score(y_test_encoded, best_predictions)
print(f"Accuracy Score on Test Set: {accuracy:.4f}")

Best Hyperparameters: {'learning_rate': 0.3, 'max_depth': 10, 'n_estimators': 450}
Accuracy Score on Test Set: 0.9985

# Converting ensemble predictions into binary classes
ensemble_classes = np.round(ensemble_predictions)

# Calculating ensemble accuracy
ensemble_accuracy = accuracy_score(y_test_encoded, ensemble_classes)
print(f"Ensemble Accuracy: {ensemble_accuracy:.4f}")

# Training XGBoost Model on the ensemble predictions
xgb_model = xgb.XGBClassifier(n_estimators= xgb_n_estimators, max_depth = xgb_max_depth, learning_rate = xgb_learning_rate)
xgb_model.fit(ensemble_predictions, y_test_encoded)

# Predicting using the XGBoost Model
xgb_predictions = xgb_model.predict(ensemble_predictions)

# Calculating the XGBoost Model accuracy
xgb_accuracy = accuracy_score(y_test_encoded, xgb_predictions)
print(f"XGBoost Accuracy: {xgb_accuracy:.4f}")

Ensemble Accuracy: 0.8376
XGBoost Accuracy: 0.9985

## XGBoost Predictions
fig = create_table_with_formatting(y_test_encoded, xgb_predictions, customerID)
fig.show()

# Calculating confusion matrix for ensemble predictions
ensemble_confusion_matrix = confusion_matrix(y_test_encoded, ensemble_classes)
print("Ensemble Confusion Matrix:")
print(ensemble_confusion_matrix)

# Extracting values from Ensemble Confusion Matrix
ensemble_TP = ensemble_confusion_matrix[1, 1]
ensemble_FP = ensemble_confusion_matrix[0, 1]
ensemble_TN = ensemble_confusion_matrix[0, 0]
ensemble_FN = ensemble_confusion_matrix[1, 0]

# Calculating precision, recall, F1 score, Sensitivity and Specificity for Ensemble model
ensemble_precision = precision_score(y_test_encoded, ensemble_classes)
ensemble_recall = recall_score(y_test_encoded, ensemble_classes)
ensemble_f1 = f1_score(y_test_encoded, ensemble_classes)

ensemble_sensitivity = ensemble_TP / (ensemble_TP + ensemble_FN)
ensemble_specificity = ensemble_TN / (ensemble_TN + ensemble_FP)

Ensemble Confusion Matrix:
[[238 145]
 [ 69 866]]

# Calculating confusion matrix for XGBoost predictions
xgb_confusion_matrix = confusion_matrix(y_test_encoded, xgb_predictions)
print("XGBoost Confusion Matrix:")
print(xgb_confusion_matrix)

# Extracting values from Ensemble Confusion Matrix
xgb_TP = xgb_confusion_matrix[1, 1]
xgb_FP = xgb_confusion_matrix[0, 1]
xgb_TN = xgb_confusion_matrix[0, 0]
xgb_FN = xgb_confusion_matrix[1, 0]

# Calculating precision, recall, F1 score, Sensitivity and Specificity for XGBoost model
xgb_precision = precision_score(y_test_encoded, xgb_predictions)
xgb_recall = recall_score(y_test_encoded, xgb_predictions)
xgb_f1 = f1_score(y_test_encoded, xgb_predictions)

xgb_sensitivity = xgb_TP / (xgb_TP + xgb_FN)
xgb_specificity = xgb_TN / (xgb_TN + xgb_FP)

XGBoost Confusion Matrix:
[[382   1]
 [  1 934]]

# Creating a DataFrame to store the metrics
metrics_df = pd.DataFrame({
    "Metric": ["Precision", "Recall", "F1 Score", "Sensitivity", "Specificity"],
    "Ensemble": [ensemble_precision, ensemble_recall, ensemble_f1, ensemble_sensitivity, ensemble_specificity],
    "XGBoost": [xgb_precision, xgb_recall, xgb_f1, xgb_sensitivity, xgb_specificity]
})

# Print the metrics table
print(metrics_df)

        Metric  Ensemble   XGBoost
0    Precision  0.856578  0.998930
1       Recall  0.926203  0.998930
2     F1 Score  0.890031  0.998930
3  Sensitivity  0.926203  0.998930
4  Specificity  0.621410  0.997389

# Defining class labels
class_labels = ["Negative", "Positive"]

# Setting the figure size
plt.figure(figsize=(10, 5))

# Plotting Ensemble Model Confusion Matrix 
plt.subplot(1, 2, 1)
sns.heatmap(ensemble_confusion_matrix, annot=True, fmt="d", cmap="Blues", xticklabels=class_labels, yticklabels=class_labels)
plt.title("Confusion Matrix - Ensemble Model")
plt.xlabel("Predicted")
plt.ylabel("Actual")

# Plotting XGBoost Model Confusion Matrix
plt.subplot(1, 2, 2)
sns.heatmap(xgb_confusion_matrix, annot=True, fmt="d", cmap="Blues", xticklabels=class_labels, yticklabels=class_labels)
plt.title("Confusion Matrix - XGBoost Model")
plt.xlabel("Predicted")
plt.ylabel("Actual")

plt.tight_layout()
plt.show()

# Obtaining the predicted probabilities from the XGBoost model
xgb_probabilities = xgb_model.predict_proba(ensemble_predictions)[:, 1]

# Computing ROC curve and AUC for the XGBoost model
fpr_xgb, tpr_xgb, _ = roc_curve(y_test_encoded, xgb_probabilities)
roc_auc_xgb = auc(fpr_xgb, tpr_xgb)

# Plotting the ROC curve for the XGBoost model
plt.figure()
plt.plot(fpr_xgb, tpr_xgb, color='darkorange', lw=2, label='XGBoost (AUC = %0.2f)' % roc_auc_xgb)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic')
plt.legend(loc="lower right")
plt.show()

	Customer ID	Gender	Age	Married	City	Zip Code	Latitude	Longitude	Number of Referrals	...	Payment Method	Monthly Charge	Total Charges	Total Refunds	Total Extra Data Charges	Total Long Distance Charges	Total Revenue	Customer Status	Churn Category	Churn Reason
0	0002-ORFBO	Female	37	Yes	Frazier Park	93225	34.827662	-118.999073	2	...	Credit Card	65.6	593.30	0.00	0	381.51	974.81	Stayed	NaN	NaN
1	0003-MKNFE	Male	46	No	Glendale	91206	34.162515	-118.203869	0	...	Credit Card	-4.0	542.40	38.33	10	96.21	610.28	Stayed	NaN	NaN
2	0004-TLHLJ	Male	50	No	Costa Mesa	92627	33.645672	-117.922613	0	...	Bank Withdrawal	73.9	280.85	0.00	0	134.60	415.45	Churned	Competitor	Competitor had better devices

	Customer ID	Gender	Age	Married	City	Zip Code	Latitude	Longitude	Number of Referrals	...	Payment Method	Monthly Charge	Total Charges	Total Refunds	Total Extra Data Charges	Total Long Distance Charges	Total Revenue	Customer Status	Churn Category	Churn Reason
0	0002-ORFBO	Female	37	Yes	Frazier Park	93225	34.827662	-118.999073	2	...	Credit Card	65.6	593.30	0.00	0	381.51	974.81	Stayed	NaN	NaN
1	0003-MKNFE	Male	46	No	Glendale	91206	34.162515	-118.203869	0	...	Credit Card	-4.0	542.40	38.33	10	96.21	610.28	Stayed	NaN	NaN
2	0004-TLHLJ	Male	50	No	Costa Mesa	92627	33.645672	-117.922613	0	...	Bank Withdrawal	73.9	280.85	0.00	0	134.60	415.45	Churned	Competitor	Competitor had better devices
3	0011-IGKFF	Male	78	Yes	Martinez	94553	38.014457	-122.115432	1	...	Bank Withdrawal	98.0	1237.85	0.00	0	361.66	1599.51	Churned	Dissatisfaction	Product dissatisfaction
4	0013-EXCHZ	Female	75	Yes	Camarillo	93010	34.227846	-119.079903	3	...	Credit Card	83.9	267.40	0.00	0	22.14	289.54	Churned	Dissatisfaction	Network reliability

	Age	Number of Dependents	Zip Code	Latitude	Longitude	Number of Referrals	Tenure in Months	Avg Monthly Long Distance Charges	Avg Monthly GB Download	Monthly Charge	Total Charges	Total Refunds	Total Extra Data Charges	Total Long Distance Charges	Total Revenue
count	7043.000000	7043.000000	7043.000000	7043.000000	7043.000000	7043.000000	7043.000000	6361.000000	5517.000000	7043.000000	7043.000000	7043.000000	7043.000000	7043.000000	7043.000000
mean	46.509726	0.468692	93486.070567	36.197455	-119.756684	1.951867	32.386767	25.420517	26.189958	63.596131	2280.381264	1.962182	6.860713	749.099262	3034.379056
std	16.750352	0.962802	1856.767505	2.468929	2.154425	3.001199	24.542061	14.200374	19.586585	31.204743	2266.220462	7.902614	25.104978	846.660055	2865.204542
min	19.000000	0.000000	90001.000000	32.555828	-124.301372	0.000000	1.000000	1.010000	2.000000	-10.000000	18.800000	0.000000	0.000000	0.000000	21.360000
25%	32.000000	0.000000	92101.000000	33.990646	-121.788090	0.000000	9.000000	13.050000	13.000000	30.400000	400.150000	0.000000	0.000000	70.545000	605.610000
50%	46.000000	0.000000	93518.000000	36.205465	-119.595293	0.000000	29.000000	25.690000	21.000000	70.050000	1394.550000	0.000000	0.000000	401.440000	2108.640000
75%	60.000000	0.000000	95329.000000	38.161321	-117.969795	3.000000	55.000000	37.680000	30.000000	89.750000	3786.600000	0.000000	0.000000	1191.100000	4801.145000
max	80.000000	9.000000	96150.000000	41.962127	-114.192901	11.000000	72.000000	49.990000	85.000000	118.750000	8684.800000	49.790000	150.000000	3564.720000	11979.340000

Customer Status	Churned	Joined	Stayed
Contract
Month-to-Month	71.784230	42.392279	62.284131
One Year	85.050904	35.622917	62.390404
Two Year	86.777083	57.695455	60.781081

	Customer Status	Churned	Joined	Stayed
Contract	Multiple Lines
Month-to-Month	No	64.946474	41.740937	54.533750
Month-to-Month	Yes	86.868837	59.691250	79.809915
One Year	No	77.111017	34.409091	50.719880
One Year	Yes	95.886022	72.100000	80.950090
Two Year	No	67.695000	54.983333	44.332596
Two Year	Yes	95.341429	69.900000	75.144545

	Customer Status	Churned	Joined	Stayed
Contract	Internet Service
Month-to-Month	No	19.356566	19.972531	20.314202
Month-to-Month	Yes	75.119923	57.156504	70.645581
One Year	No	21.138889	18.543333	20.339766
One Year	Yes	88.714650	64.088889	76.517436
Two Year	No	22.700000	21.100000	21.266851
Two Year	Yes	94.227907	68.458824	81.926715

Customer Status	Churned	Joined	Stayed
Contract
Month-to-Month	14.016918	1.705882	25.042663
One Year	44.963855	1.875000	41.494853
Two Year	61.270833	1.818182	54.353006

	num__Age	num__Number of Dependents	num__Latitude	num__Longitude	num__Number of Referrals	num__Tenure in Months	num__Avg Monthly Long Distance Charges	num__Avg Monthly GB Download	num__Monthly Charge	num__Total Charges	...	cat__Unlimited Data_Yes	cat__Contract_Month-to-Month	cat__Contract_One Year	cat__Paperless Billing_No	cat__Paperless Billing_Yes	cat__Payment Method_Bank Withdrawal	cat__Payment Method_Credit Card
0	-0.579600	-0.491619	-0.554996	0.351930	-0.006926	-1.063957	1.253468	-0.238987	0.018307	-0.811689	...	1.0	0.0	1.0	0.0	1.0	0.0	1.0
1	-0.045161	-0.491619	-0.824038	0.720843	-0.668349	-1.063957	-0.796233	-0.533037	-2.219753	-0.834158	...	0.0	1.0	0.0	1.0	0.0	0.0	1.0
2	0.192368	-0.491619	-1.033094	0.851324	-0.668349	-1.272578	0.688345	0.447129	0.285202	-0.949616	...	1.0	1.0	0.0	0.0	1.0	1.0	0.0
3	1.855067	-0.491619	0.734020	-1.093819	-0.337638	-0.897060	0.311381	-0.827087	1.060162	-0.527161	...	1.0	1.0	0.0	0.0	1.0	1.0	0.0
4	1.676921	-0.491619	-0.797613	0.314431	0.323785	-1.314302	-1.010255	-0.484029	0.606762	-0.955553	...	1.0	1.0	0.0	0.0	1.0	0.0	1.0